Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Sampling from a k-DPP without looking at all items
Authors: Daniele Calandriello, Michal Derezinski, Michal Valko
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 18,122 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,136 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate our -DPP sampler on a benchmark introduced by [13] (see Appendix D). The benchmark uses subsets of the infinite MNIST dataset [33] with d = 784 and n varying up to 10^6. All experiments are executed on a 28 core Xeon E5-2680 v4. Results We begin by reporting results on a smaller subset of data (Figure 1) where even the nonefficient samplers can be run. |
| Researcher Affiliation | Collaboration | Daniele Calandriello Deep Mind Paris EMAIL Michaล Derezi ลski University of California, Berkeley EMAIL Michal Valko Deep Mind Paris EMAIL |
| Pseudocode | Yes | Algorithm 1 -DPP sampler; Algorithm 2 Binary search for initializing the k-DPP(L) sampler |
| Open Source Code | Yes | Our implementation of -DPP is provided at https://github.com/guilgautier/DPPy/. |
| Open Datasets | Yes | The benchmark uses subsets of the infinite MNIST dataset [33] with d = 784 and n varying up to 10^6. |
| Dataset Splits | No | The paper mentions using the MNIST dataset but does not explicitly provide details on training, validation, or test dataset splits, percentages, or sample counts for reproduction. |
| Hardware Specification | Yes | All experiments are executed on a 28 core Xeon E5-2680 v4. |
| Software Dependencies | No | The paper mentions that algorithms are implemented in python as part of DPPy [20] but does not provide specific version numbers for Python or DPPy. |
| Experiment Setup | Yes | We use an rbf similarity with ฯ = 3d, and set k = 10 to match the number of digit classes in MNIST. |